Core Motion leverages the M7 motion coprocessor and other built-in sensors to provide support for step counting and motion tracking. See examples of Core Motion based apps in action. Learn how health and fitness apps use pedometer information to give users performance and workout results, and how journaling apps use motion classification to tag the day's activities.
My name is Andy, and I'm here to talk about Motion Processing.
So welcome. We're going to start with some definitions.
We'll flesh out that term "motion processing" by giving you a description of the features that comprise it.
We'll talk about how they work, as well as how well they work.
We'll give you some ideas on how to use it in your application. We'll help answer some questions about when should I use one or the other, what interface should I use, and how can I use it with other services from the OS? From this very high level then we'll dive into the details of the interface, we'll talk about usage, we'll deconstruct the data objects so that you can know better what to expect, and finally we'll reiterate the main points with a coding exercise.
Now before I go any further, I do want to apologize because I've been standing up here saying motion processing, motion processing.
The title of this track is "Motion Tracking", and this session is not Gaming, so there's probably a huge number of you who are in here thinking that you're going to learn all about how to track a person's motion in a gaming context.
This is not that talk, so nevertheless I hope you'll stick around because there's a lot of really neat things that we're going to reveal and hopefully even if you weren't planning to use this, at least it'll spark some ideas on how you can perhaps develop the next great app with this. So, with that, let's go on.
The biggest thing that happened since the last time we talked, Core Motion, has been the advent of the M7 Motion Coprocessor.
This is an ultra-efficient microcontroller that we use to essentially manage all of our sensory interaction.
It's really cool, but for the purposes of this talk, I'm really just going to focus about how we combine the M7 with an electron sipping accelerometer to give you motion awareness, constant motion awareness, with no noticeable impact on battery life.
How efficient is our motion processing system? You get 24 hours of motion activity pedometer for about three minutes of a FaceTime call.
So what can you do with motion activity? I think that when you're talking about a mobile application, where you have limited real estate, you have limited bandwidth, and the user is always in a hurry, context is key, right? It's the filter that allows you to filter out the nonessential information so that the really important stuff just comes to the top and it allows the user to connect the dots so that they can bridge missing information.
It allows you to relate information from disparate sources so that you can recognize patterns, right? Now I don't have to convince you of how important location is as a context, it's intuitive and you're all aware of that, right? I do a search for cafes in London, I would expect different results if I had initiated that search in Ohio versus England, right? Time is a context. Activity is similarly very, very powerful as a context, right? And that's what we're here to give you, the ability to know the user's motion at any time so that you can frame the context. So not all activities are going to be detected in the same way, there are going to be differences, and I want you to be aware of that because that's going to drive different use cases. Different activities are going to be better suited for different ways or different applications. So please be aware. And then when we talk about performance we're really going to evaluate on three things.
We're going to look at detection accuracy, we're going to talk about the latency of detection and then, finally, we're going to talk about the robustness, how sensitive are we to differences in, say, the device location or situational differences. So, again, please bear this in mind.
And when I start talking about the performance in a little bit, I'm going to stay very qualitative.
I will give you some numbers, but the intent is not to give you precise numbers and not to compare the performance to our competitors. We are very proud that we have industry leading performance, but we do encourage you, I encourage you to please go out and test for yourself and quantify for yourself how well this will work for you when you get the final seed. So, with that, let's go ahead and get started.
The first activity I'm going to talk about is the walking activity.
This is something that most people do every day, it occurs quite often, so you're going to see this a lot. When we talk about the performance and how the performance changes across location, it's very, very robust. When you're walking, we're going to detect it, and it doesn't really matter if the device is in the pocket, on the waist, on the arm, or even in hand. Now I do want to caution, though, that when a person - it is possible that a person could hold a device so steady while they're walking, such as when they're trying to read a text or so on, so that you can fall out of walking, but again they're not going to do that over the entire walk so that's okay.
In terms of latency, it's fairly low, 5 to 10 seconds.
If it's in my pocket and I get up and I start walking it's going to detect that in about 3 to 5 seconds. It's going to take longer, as I said, if it's in the hand or the user is fiddling with that. So that should be your expectation. In terms of accuracy it's very accurate.
You walk, we're going to detect it. And I'm going to say it's accurate on average because you can expect that you're going to intermittently fall into and out of the walking state, but again that's okay, right, because that's actually the situation that the user finds himself in.
Let me explain what I mean by that.
When you get up and you walk a lot of times you may get distracted or you may stop, say, to open a door, or somebody stops you to chat.
And so as you stop and as you start you're going to fall into and out of the walking state, and so the application, going back later in time to look at say the query, the history of transitions between the start of the walk and the end of the walk you'll see that it's mostly walk and sometimes you'll be in a non-walking state, but again, that's okay and as long as you expect that and deal with that appropriately it should be okay.
But what does that mean for how you can use walking? You should use walking, if you're thinking about using walking as a context in the here and now, you'll want to be careful about that because walking is not a context here and now, walking is a context over the past say 30 seconds, right? If I've been walking for the past 30 seconds I'm walking, but I could be instantaneously stopped. I think walking might be better suited to use as a transition, and I'll talk a little bit later in the presentation, it'll be more apparent what I mean by transition, but walking is how we go from A to B, right? And so if you keep that in mind it'll be a very, very powerful tool for you to help infer what the situational context was.
All right, next is running.
Running is really great.
Again, in terms of robustness, it doesn't matter where you hold it. We're going to detect running, and it's going to be pretty uniform in the performance. What's great about running is that not only is the signal very, very vivid, in other words, it allows us to quickly detect that a person is running. You take a handful of steps, a handful of steps, and we're going to detect running.
So not only is it very, very vivid, but there is no situational ambiguity.
If a person runs they really mean it, right? They're not going to multitask while they run, as like they do while they're walking, right? When they're running they're not going to log into Facebook and change their relationship status, they're just going to run. And when they stop it's not because they saw something shiny on the side of the road and just got distracted. No, they stop because they meant to stop.
So as a context in the here and now running is very, very great, right? Short in terms of latency; very, very short in terms of detection; very, very accurate; and in terms of robustness completely robust, so think about that, just think about how you'd want to use that.
We've already talked about the accuracy. So let's go on to driving, driving is different from running and walking because the motion signature is more subtle, and because it's more subtle.
The more coupled the device is to the vehicle dynamics, the more likely it is that we'll be able to detect the driving state much more quickly. So if the user were to, say, get in the car and put the device on the dash, in the cup holder, perhaps mounted, that's fine. As soon as they drive off we're going to be able to detect driving.
Conversely, if the user were to, say, have the phone in their back pocket, and it would take a lot longer to detect driving, but again that's fine because eventually we'll detect driving, and if you use driving as, again, defining a transition or if we were going to use that with walking, I'll show you again later in the presentation how you want to do that, you can make up for some of these challenges from observing things strictly through motion.
Now the latency, again, is variable, as I just described.
Now what's really cool is that we're going to use information from other sources as it becomes available. Without really going into details what it really means is that if you get in your car, you plug your device in or you have Bluetooth or so on and so forth, it's going to be able to detect your driving state pretty quickly, so that's really cool. Cycling is new, something we introduced in iOS 8.
Cycling is very challenging, and again you need the dynamics and so it's going to be very sensitive to location. If it was mounted on the upper arm the latency is going to be fairly reasonable. And if it's anywhere else, it's going to take a lot longer. So definitely I would not suggest using cycling activity classification as a hint for the context here and now. It's really something that you'll want to use in a retrospective manner for a journaling app, for example. This would be a perfect use case for that, right? So that's motion activity. Now I want to talk a little bit about the pedometer.
For the pedometer you have two things, you have step counts and you have the stride estimate, how long is your step.
With step counting there is a surprising amount of value in just the user knowing their step count, right? There's a lot of research already out there that suggests that at least for sedentary, for people with a very sedentary lifestyle, the best thing they can do for themselves is to just to get up and walk, just take steps. And we know that, say, the average American only takes around 5,100 steps a day. If you are from Switzerland, Western Australia, interestingly, you'll take about 9,500 steps.
And so what you see here is a great opportunity, it's a great opportunity to provide something that will really help users improve their quality of life, just knowing that I want to target 7,500 to 9,000 steps or, if I want to be more active, try to get over 10,000, and giving me constant feedback about how I'm doing, that would be great.
I think that would just really make a whole world of difference.
But if you wanted more than just step counts and just wanted more than knowing how active you are, then stride estimation gives you more quantitative results, right? With stride estimation we can tell how far your walk was, and so if you were to like, say, initiate an activity, go out and hike, go out and run, we would be able to say at the end of it that you ran four miles or that you hiked three-and-a-half miles.
And then, also, if you were to look at how we do, the user ran or hiked in short bursts then you can see the intensity of the workout, and again that is something that is very interesting and very powerful to present to the user, right? Now you can argue that you could do this with GPS, and while that's true, the attendant power cost in GPS is literally 100 times greater than the power of the speedometer, the energy cost of the speedometer. So you can give, what if you can give the same performance in terms of the distance without that power, and what if you can give the same performance in that distance no matter, say, the GPS conditions, whether it's urban, canyons, trail hiking, or suburban streets, you get the same performance, wouldn't that be cool? So talk a little bit then about the performance of our pedometer.
Similar to the activity classification, the step counting is very consistent across body location, and that's something that we're very, very proud of. Again, it doesn't matter whether a device is, here let's say, FitBit.
The device is here, it provides very, very good clean signature of your motion, and so they're able to get a very, very accurate count to your step.
We're able to provide similar levels of accuracy, but not only just, not only here, but in the hand and everywhere else, so that's really cool.
Just talked about accuracy.
Another thing that we really focus on is we try to be robust to extraneous inputs.
It is accelerometer-based, and so you'll see things, such as periodic inputs into the device that will generate steps.
We try very hard to eliminate most of that, and we actually do very, very well. Compared to our competitors, we're actually much, much better. So try this for yourself, get a Fitbit, hop on a bus, sit in the back of the bus, more towards over the wheel well, right? Take it up and down the 280, you'll see that you'll walk out of there and will have counted, say, 1,000 steps, but again that's a challenge of a pedometer or an accelerometer solution.
So stride estimation, this is again just like the step count, we're very insensitive to the body location, but and this is the really important thing, we're also insensitive across pace.
It doesn't matter if you're running or jogging or hiking you're going to get the same accuracy, and that same accuracy, like I said, is pretty good, very, very good.
And the other great thing is that it's going to adapt to the user across over time. So the more they use it, the more accurate it's going to get.
So, again, I encourage you when the final release comes out, do go try it out for yourself, take the device, strap it on, walk around the block, maybe walk around the block a few times and see how well we do.
Okay, so how can we use motion activity? I want to start by talking about the different data models that we use or the different ways that we can get the data to you.
There's a notion of push versus pull.
In the push interface, we give you events as they happen. This is our lowest latency path, so if latency is important to you, you'll want to use this interface. What that means is that when motion state transitions happen, we'll let you know right away. Every two-and-a-half seconds, we'll give you the new step count, that's the lowest latency. You can also use the query interface, and in the query interface you will provide a range of times, a start time and an end time.
And for motion activities, we will give you basically every transition that has happened between the start and end time.
For the pedometer we'll just give you the cumulative steps, so you'll get a single record that provides a cumulative pedometer activity between the start and end time.
So you understand latency, but then what is the value of the query interface? For that let me talk a little bit about the architecture, so we'll start with the accelerometer. And as you can probably surmise we then operate in the data at a periodic interval.
I mentioned two-and-a-half seconds earlier for the step count. It's actually what cadences that run everything. Now I'm telling you this not so that you set your watches by it or that you design around this, I'm really telling you this, two-and-a-half seconds, so that you can get an expectation for the latencies, right? And so when I say running, we can detect that you're running in three to five seconds, that's really, that three to five seconds that's because of that two-and-a-half second batch processing that we do. And really as soon as we just look at data over that interval and you're running, we're going to know you're running right away, right? And so this information as it happens gets pushed to the client, and so that's the push interface.
Now what's interesting is that we also filter the information, so we'll take all of these state transitions in, and we'll try to fuse them with data from other sources, and then we'll persist that.
And so if an application were to come back later in time and ask for the data, the information is going to be more accurate and it's going to be less intermittent. We talked about walk and how you can start and stop, and stop and start, and start and stop. With the query interface, many of those transitions will be smoothed out for you.
So I think it might be more tangible if I were to motivate how we use this in an application.
So let's say running, let's come up with a running application. I mentioned running as one of those great activities that gives you the context here and now. I'm going to see how we can use that.
So in a running application, I'm going to be running, so the last thing I want to do is to mess around with the interface or do anything with it, right? I just want to launch it, and I just want it to understand what I'm doing and try to react to me rather than what I do to it. And so I wanted to automatically change this experience, my experience as I go through my exercise.
While I'm exercising I want it to give me constant feedback on how well I'm doing.
And then it'd be nice that once I stopped it could automatically, say, summarize my results and present that to me, right? So, let's think about how that could happen. So let's say I've got this running app, I'm going to launch it, I'm going to strap it on, and then I'm just going to go through my stretches.
Now music is a really important part of my experience when I exercise, and so let's say that it's going to start just playing some kind of preprogrammed, easy listening kind of playlist. Now my stretch and my warm-up routine, it's not constant, so some days it might be two minutes, some days it might be 10 minutes. And so I don't want to design a playlist around my stretch workout, I just want to put something in there.
And then when I start running it'd be nice if the OS detected that, and then the app were then to quickly or just automatically change out my playlist, so to something that's more uptempo, right? Don't even have to think about it, I just start running and then, boom, the music changes.
So I'm running, and now you can imagine the Siri voice coming out over my earphone, giving me just this great feedback, "Andy, you're doing a great job, you're on pace for your 15-minute mile." So just stuff that gets me really pumped up.
And you can imagine I hit my first mile and it tells me, you know, what my split time is, right? So it's really real cool. I'm getting this constant feedback, and then I get to the end of my run and I slow down, and that music is really bothering me, so it switches automatically to a cooldown playlist, something more suited to my walking pace now.
And I don't even have to think about it, right? It just happens, right? And then, let's say, I'm done walking, I'm done with cooldown, and automatically just, say, maybe it uses a query interface to loop back over how I've been doing the past several days and compares my performance historically, or it might summarize my run in that particular day. So all of that basically you can get for very little cost with the services that we're providing you in Core Motion, so that's pretty cool.
So that's an example of a running application where you rely primarily on the push interface, you're using context in the here and now, but let's talk about a journaling app, where the requirements might be different.
And so what would it - why would I want a journaling app? I think in a journaling app what really matters to me is context and correlation, right? But I want the app to do that for me.
I want it to be able to figure out what I was doing and where, so I'd want it to correlate multiple sources of information.
And then ideally I would like for it to profile my physical activity, let me know how active I've been through the past day.
So, this is what my typical day looks like, and so let's say this journaling app would start by querying the visit monitoring service, which is a service from the location, Core Location, and it gives me a list of places that I've visited throughout the day.
Typically I go from home, sometimes I go to the gym, and then of course I go to work, and so it detects all that, and so that provides essentially the location context.
Now suppose the journaling app also then queries for the transport context between these points? And so looking at motion activity it knows that, oh, I drove from home to the gym instead of cycling and doing a solid for the environment, just really lazy.
Got to the gym, and then from the gym I walked to work.
And what if the app then queried the pedometer, and then I could look and see, say, how far was the walk between work and the gym and how many steps that I took in each of those different places. And you can see here that this presentation of these results is so much more powerful than to, say, that I walked 5,000 steps, right? To say that I walked 5,000 steps, if I were to look back at that data, five days or 10 days from now it would mean very, very little to me.
But if I looked at that data and it was presented something like this I would know that, oh, on that day I lost my keys in the morning.
That's when I walked around, all over the house. All right, so that's some examples of how to use the services, how to combine it with other OS services, and the difference between a push and a pull interface.
So now let's go a little into the, basically the API. Before I do that, I want to talk about privacy. It's very important to us, privacy, we treat it very, very seriously.
And so, for motion, just like location, the user is going to need to opt in.
Now the very first time you launch your app we're going to request access on your behalf, and a popup, a dialogue will pop up that looks something like this.
Your app name there, and then the user would then choose to opt in, and as soon as the user opts in then essentially you get data up to seven days in the past, no more than seven days, just seven days in the past.
Now it's important to note that at any time the user can opt out, so when the user opts out then you will no longer be authorized to receive the data, and then you'll have to basically be able to handle that case.
Sunny is going to show you in a bit with a coding example how you can detect that condition and how you can essentially request for reauthorization.
And so that's a bit about privacy. Now let's go on into actually the API.
So the very first thing that we want to talk about is the - for the activity classification you'll want to deal with the activity manager, so you'll get a reference to the CM activity manager object.
And the first thing you want to do is to check whether activity classification is available. It's pretty standard stuff, there's not a lot of mystery there, but I'm just pointing that out because it's just good general hygiene.
Now two ways I mentioned to get data, there's the push and the pull. For this one, this push interface, what you want to do is basically provide a handler and a queue, and then we'll call you back on that handler and we'll basically call you back. As soon as you call that start method we'll call you back right away, and what we're going to give you then is the activity that the user or the device is in at that time, okay? And then any subsequent changes after that we will then trigger an update, right? For the push, for the pull interface, again, you provide a start time and an end time in the query.
Now the reason why you want to provide a queue is because, like I said before, we're going to call you back not just once, but we're going to call you back for every activity transition that has happened between the start and the stop, so you'll basically get a flood or a storm of records, okay? And it's also important to note that the first record that you get, you'll actually get a time that is before your requested time, and that's okay because it's that current activity, that's the very first activity and its start time might be before when you asked for it, all right? Okay, so that's pretty straightforward.
The start time and the activity, and so the CMMotionActivityManager, you'll get a CMActivity object. Again, the start date, not that interesting.
This is what is very interesting, in addition to the start date of the activity we'll give you a confidence.
The confidence is what you can use to trade off the difference between accuracy - or trade off accuracy with latency.
When you start walking, you'll get walking, low confidence, potentially medium confidence, and then high confidence. Sometimes when we're really, really sure that you're high confidence walking, we might skip the ones in between, but typically the transition is going to go from low, medium, to high.
And so if you're really, really interested in latency you may want to react as soon as you get a low confidence transition, but if you want to be sure that you only react to very high confidence stuff then you want to wait, all right? Now I want to get to the actual classifications, themselves. The classifications are presented as a bunch of BOOLs that you're going to have to query one at a time. So, the very first one is this notion of stationary.
Now stationary here is in the context of motion stationary and not location stationary, right? Here I'm on the stage, I am location stationary, but I am not motion stationary, so this thing is only going to be true when the device is say on the table.
These are the actual classifications that we're going to try to make a call on - walking, running, automotive, cycling, you already know that.
Unknown, that's an interesting one. What that represents is essentially the state in which we don't have enough data in order to make a call.
When all of those BOOLs are false it means that we have plenty of data, we're just not going to make the call.
Unknown is, the unknown state there is the unknown unknown, and when they're all false it is the known unknown.
Now I know you can't appreciate this, but I've always wanted to stand on stage and say that, and so, check, bucket list.
All right, again, so to make these points really salient, let's go through some example scenarios.
So here we have the scenario that the device is going to be in and then all the BOOLs and what they're going to represent. So when the device is on the table, like we said, it's going to be stationary and, obviously, it's not going to be in any of those other states, so a trivial example, right? What happens when the device is on the runner's upper arm? In that case, running is going to be true and everything else is going to be false, so, again, not that interesting.
This is interesting, what if it was in the dash of a vehicle and it's idling at a stoplight? In this case the stationary flag is true, as well as automotive flag is true.
So stationary can be - coexist alongside the other classification states, that's an important point. And if it was in the in-dash of a moving vehicle, then stationary toggles back to false, automotive still stays true.
Immediately after the reboot, this is the case where we don't have enough data yet to make a determination, so we actually don't know what the state is, and so that's the only time when - oh, I jumped ahead - oh, so for this one, this is interesting, this is when the passenger's checking e-mail. This is because there's motion here and it's not something that we want to classify, the passenger or the vehicle is in the car, but the motion suggests that we actually don't know whether it's in the vehicle or not, so all of this is a false. We know that you, as a driver, wouldn't be checking your e-mail, so that's why we have that example.
Finally, the immediately after the reboot was what I was talking about earlier.
This is the only time when the unknown flag will be true.
So similar to checking the e-mail, what if there is motion with a very strong pattern, but like say Zumba? That one is tough because it's periodic, so in some ways it can look a lot like walking and running, but if it's on somebody that is not well practiced in Zumba or whose hips don't work that way it could look really awkward, and so from a motion signature standpoint it's something that it's just going to stay unknown for us for a while, but that doesn't preclude us from extending our classifications in the future to add activities that are interesting, all right? So, I talked earlier about how we can use walking as essentially a transition and how that can help us, so let me drive that point home.
Let's take this example, let's say I'm walking to my car, and so in that particular state, walking is going to be true. I get into my car and now I sat down, and so now walking is no longer true and all of my flags are false. I'm essentially in the unknown state. I start the engine and I start driving, and I put it down and start driving, put it down, start the engine, so it's completely stationary now, so now stationary is true. And now I start driving, and so automotive is true and stationary is false, right? I stop at a light.
I'm still in automotive, stationary is true.
Now you can imagine that you're going to cycle back and forth on this thing until you actually pick the device up and now it doesn't look like we're in automotive anymore, and then you'll go into that empty set.
What's interesting is that in the driving context you'll basically limit cycle among these four states without actually being automotive equals true.
You can think automotive equals true as a hard classification, and you can think of those other two states essentially being an inference that you're still driving, and that you're going to stay driving until you walk away, right? And in this case, walking serves as a boundary condition that defines driving in a larger context.
Does that make sense? All right.
That's pretty cool. All right, so now let's talk about the pedometer API.
So, again, you'll want to check whether it's available. It's not going to be available on all devices. We're going to, and when I say it's not available on all devices, it's not going to be available on all M7 devices, we're only going to support a subset of them, so you'll want to check which ones that are supported.
All right, so similar to the activity, the push interface, you provide a start call, and then we'll start giving you updates.
Now the one thing that I didn't mention earlier is that we don't always provide an update.
I said earlier we were going to give you an update every two-and-a-half seconds. Well, that's not strictly true.
We'll give you an update every two-and-a-half seconds as long as you're taking steps.
As soon as you stop taking steps, we won't give you an update, but again that makes sense. So we'll give you an update when something interesting is happening.
What's interesting is that the only time we violate that is when your app gets backgrounded and it gets resumed.
In that case, as soon as you get resumed, regardless of whether you've taken any steps or the user has taken any steps you're going to get an immediate update, all right? As far as the pull interface, again, you're just going to give us start and end, and we're just going to give you everything that happened in between. So there's really not a lot to say about that. Start and stop time, again, that's going to mark the - what's interesting here is that for the pedometer, every record that you get you'll have the same start time, and that start time is the time when you call the start update method.
That's really, really important, so every record that you get will have the same start time, you'll just have a different end time, okay? And that end time represents basically the cumulative number of steps taken between the start time and the end time, so you can think of every record essentially just providing a cumulative value that is delta increment over the previous value.
The number of steps is going to be an integer number, and one of the things that we always get asked about is what happens if you provide several queries, one query over say 24 hours, and then 24 queries over an hour, should you expect to get the same number of steps? And the answer is yes, there might be slight differences due to rounding issues or interpolation issues, but by and large the number of steps should be the same.
And the reason why you'd want a large query, in addition to smaller queries, again, it depends on your use case. If, say, for example, you know that a person left the particular location at a certain time and ended in a location then you want to just provide a query over that time interval, right? All right, total distance, again, similar to the number of steps, except distance is going to be a fractional number and that's going to be represented in meters. Again, that is the total distance that you travel, it could be from start to stop, so it's not the actual stride themselves.
All right, so that is pretty briefly the Motion Processing services that we provide.
The APIs are pretty straightforward, pretty simple, but you'll find that the things that you can do with them can potentially be very, very powerful.
The stride estimate, I think can basically facilitate a whole new set of applications around health and fitness.
And you can imagine now using health or the things that you achieve in fitness as an additional currency. If you've not seen the HealthKit session, I encourage you to watch that on video because that perhaps will give you some ideas on how you can use this in a social media context, all right? So that's it, all that I have. I'm going to invite Sunny up to actually go over the demo exercise.
Thank you, Andy.
Hello, everyone. My name is Sunny. I'm a Developer on the Core Motion Team.
So the topics I want to be covering today are mainly about CMMotionActivityManager, CMPedometer, as Andy just covered. We'll be taking a deeper look at both of these classes during the coding exercise.
Now as we're going through coding exercise, at times there might be a lot of code that's displayed all at once. Don't worry too much about following every last detail, though.
This coding exercise has already been made publicly available.
The important parts, though, I will be sure to highlight, and I think as long as you follow those parts it'll make your experience with the Motion Activity APIs that much smoother.
So let's go ahead and start with the coding exercise.
Here you go. So just some context - so this coding exercise eventually compiles into a UI application, and the code that we're about to implement right now makes up the data model that the UI application will use.
The very first thing that we want to do with this data model is, as Andy mentioned, we want to do - we want to check for proper availability of these APIs before we start using any of the queries or the live updates. So let's go ahead and go implement that.
Get rid of that typo there, and the check for availability looks something like this.
And you look at the parts I have highlighted. Here it's as simple as checking for activity that's available for CMMotionActivityManager and isStepCountingAvailable for CMPedometer? It's important to check for both because, as Andy mentioned, not all platforms support both. Now that we've done our due diligence it's time for us to create our instances of CMMotionActivityManager and CMPedometer.
Something to keep in mind with these classes is that a lot of our APIs are largely callback-based, so you want to keep in mind about the lifetime of these objects.
For my class, because I'm expecting or will be expecting live updates at any point during my program, I've decided to tie really the lifetime of these objects with my data model, in essence tie the lifetime of my data model with my application. A pattern I've seen commonly with code that has come across my table is people will very commonly create an instance and assign it to an object that only lives on the stack, and so what happens when that object perhaps in a stack frame goes out of scope? Well, that object is released, and no more callbacks are called. So it's important that you keep in mind about lifetimes of these objects. You just want to make sure that these objects are still available when callbacks, when you want callbacks to be involved. The next thing we want to do is check for authorization. This is - we handle most of the details of authorization for you, right? Every time you make a call that accesses motion activity, we'll do the proper popups. The only time you'll want to do something more than that is to check to make sure that your app does have authorization to access activity because when you don't, you want to show a dialogue box saying, informing the user of his mistake and allowing your app a chance to correct that behavior.
So the query looks like this. As Andy mentioned, there's - well, the authorization really is just a simple query. We don't really care about the results that come from this query, we just care about sort of whether it returns an error or not. This is why the "from" dates and "to" dates are both equal to now.
Now on the callback we just want to make sure that there's no errors, and if there is an error code that it's not equal to this particular error message. And if you've done that then you've made sure that your app is, indeed, ready to start querying for motion activity data.
There's one more thing to notice here, I've chosen to use CMPedometer to check for this authorization, and because my authorizationCheck CompletionHandler may possibly invoke some UI dialogue informing the user that motion activity was denied, you will want to make sure that you dispatch that block onto the main queue because blocks that are passed to CMPedometer will be invoked on an arbitrary queue that might not necessarily be your main queue.
Okay, so now that we've done our check for authorization, now it's time for us to start querying data, and this is kind of the magical part of our API because it allows your app to give the illusion that you've been collecting data all along.
So the queries, for this data model, this data model is responsible for collecting data from start date to end date for both motion activity data and step counting data, and when all of that data has been collected then it's time to invoke some sort of completion handler allowing the UI to be updated with all of that data.
So the motion query looks something like this.
There's a start date, there's an end date, there's a main queue, and there's a completion handler.
Be sure to check for errors because at any point during your program's operation the user can still go ahead to the security screen or the privacy pane and disable authorization on your app.
After that, you can start playing around with the activities that are passed back when this call back is invoked.
Now a word about the types of data segments that are passed back after this query, very often you might not be interested in all the data segments that are passed back.
For example, you might not be interested in all the periods of time for which your device stays static or that, if you're a fitness app you might want to consider a trip, a jog through a shopping mall as one bit contiguous walking segment as opposed to a walking, stopping, walking, stopping segment.
And so you'll want to do some sort of app-specific filtering on this activity data to kind of clean up and make it appropriate and more sort of easier to understand for the user. And this is what, this additional processing method is doing, it's doing some filtering that's specific for the demo app. In the demo app, we'll do some filtering based on confidence, we'll do some filtering based on getting rid of data seconds that are short enough and have no sort of classification behind them.
But I'll leave the sort of implementation details up to you as an exercise for you to check out later.
Now that we've done that, we have activities ready, they're filtered, they're almost - we're almost ready to push up and let the UI know that all of our data is ready, but as I mentioned we're also interested in step count data.
The question that comes up now is, well, how do I synchronize my motion activity results with my step counting results, and what I like to do is to solve that problem and nest the query so that one successful query results in another query, and at the end of all the successful queries is when you tell the UI is ready to display the data.
The step count query looks like this.
There's a start date, there's an end date.
Note again the absence of an operation queue parameter, this means that if you were to invoke any UI modification behavior inside your completion block, that it's wise to dispatch your first domain. And let's go ahead and invoke the completion block that ultimately invokes the UI.
And that's it, at this point my app has all the data that was accumulated on behalf of my app while my app was in the background.
So in order to further the user experience so that you can see live updates as the user is using the device, let's go ahead and start using the live updates parts of our API.
So we'll start with step updates, it's as simple as calling starts, and for people who are used to seeing step counter you might have noticed that we have a new parameter here and a state, this is a convenience for you so that in case you want to start step counting, but you want some offset applied to it, say you're a daily journaling app, you want step counts to be counted from the beginning of today, not from right now.
You can now specify that date, that saves you an additional query and having to combine information together.
Again, we did not specify an NS operation queue because this makes the API simpler, but if you want to do any UI modification behaviors, be sure to dispatch this back to the main queue before you run your completion handler. I know I'm sounding like a - I'm repeating myself a couple of times, but this is something that I've found something to be problematic in my own app development.
Stopping step updates is simple, you just call stop.
Let's go ahead and start motion updates, as well. Starting motion updates is very simple, specify queue, specify a completion block, and they'll all be kind of handled for you, and you'll have indications every time you get a new motion activity.
And stopping motion updates is also as simple as calling stop.
And there you go. At this point you have a fully functional data model that you can use in your UI app to get motion activity data for when your app is in the background and for when your app is in the foreground.
So let's go ahead and take a look at what this eventually compiles into.
So here we have the motion activity demo app, and in the very first view you can see a listing of the past seven days, for which we've collected data, and if you click on any of these days you'll see sort of some transformations that we've done on the set of activity data segments.
You'll see the total durations for walking, running, driving, and moving.
And because I've clicked on the Today view you can also see my current activity, and because this device is in my hand it says it's moving.
If I set this device on the floor or on the table, I'm sorry, it will now say it's not moving.
It will also show you a set of live step counts.
What's more interesting or what's really cool about this is once you do the application-specific filtering, you can get a lot of very useful contextual information, as well.
And this is sort of the filtered history of my day so far. You can see when I walked to my car in the morning, from 8:15 to 8:20, and you can see my commute from home to work, from 8:20 to 8:36. Now I actually have a real time span for how long it took me to get to work.
You can see me walk from my car to the crosswalk in front of aisle two, and I took 50 steps, and there's a little bit of a running segment here because I was trying to beat the Ferrari that was stopped and I didn't want to hold up the exec in that Ferrari.
After that, I walked to get some breakfast, and you can kind of unfurl all of that, and you can see when I make that big long drive from aisle two to Moscone Center.
So, cool, so that's the demo app, and I've talked a lot about sort of the steps that are necessary for accessing motion activity data. I've talked about checking for availability. I've talked about initialization. I've talked about authorization. And I've talked about historical queries. I've talked about live updates.
However, nothing beats hands-on experience, so I highly encourage all of you to go ahead and download this coding exercise and compile it, deploy it in your phones, and take a look at the data that has already been collected for you, I think you'll be pleasantly surprised.
So for any questions that you might have or for more information about our Motion Activity Frameworks, feel free to contact Allan or after WWDC feel free to interact on the Developer Forums.
As Andy mentioned, motion activity is just one context, right? However, when you start combining this with other contextual queues from HealthKit, from Core Location, it can help you build a very powerful picture of what the user has been doing and what the user is now doing.
So we highly encourage you to check out the sessions related to HealthKit and Core Location, as well.
So I thank you all for attending this session. We really appreciate you taking the time to understand the Motion Activity APIs a little bit more.
We hope to see some of you at the session, and for those of you who we don't, we hope to see some of your work on that, and we can't wait to see what you will do with these - what you'll come up next with these APIs. Thank you so much.
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